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Development of a model for
the identification of suitable
areas for the development of
wave energy projects in the
European Atlantic region in the
context of maritime spatial
planning and its
implementation into a Decision
Support Tool
This Project is co-funded by the European Climate, Infrastructure and Environment
Executive Agency (CINEA), Call for Proposals EMFF-2019-1.2.1.1 - Environmental
monitoring of ocean energy devices.
WP 6
Development and implementation of Decision Support Tools for wave
energy development in the context of maritime spatial planning
Lead partner for deliverable:
AZTI
AUTHORS
Ibon Galparsoro (AZTI)
Gotzon Mandiola (AZTI)
Roland Garnier (AZTI)
Iñaki de Santiago (AZTI)
Enric Villarín (CORPOWER)
Gregorio Iglesias (UCC)
Ines Machado (WAVEC)
Thomas Soulard (ECN)
Enored Le Bourhis (ECN)
Laura Zubiate (BiMEP)
Iratxe Menchaca (AZTI)
Juan Bald (AZTI)
SUBMISSION DATE
27| September| 2022
DISSEMINATION LEVEL
PU
Public
X
CL
Classified – EU classified (EU-CONF, EU-RESTR, EU-SEC) under Commission
Decision No 2015/444
CO
Confidential, only for members of the consortium (including Commission
Services)
DOCUMENT HISTORY
Issue Date
Version
Changes Made / Reason for this Issue
27/09/2022
0.1
Ibon Galparsoro and co-authors. Final version
06/10/2022
1
Juan Bald. Reviewed version
CITATION
Galparsoro, I., G. Mandiola, G., R. Garnier, I. de Santiago, E. Villarín, G. Iglesias, I. Machado, T.
Soulard, E. Le Bourhis, L. Zubiate, I. Menchaca and J. Bald, 2022. Development of a model for the
identification of suitable areas for the development of wave energy projects in the European
Atlantic region in the context of maritime spatial planning and its implementation into a Decision
Support Tool. The corporate deliverable of the SafeWAVE Project co-funded by the European
Climate, Infrastructure and Environment Executive Agency (CINEA), Call for Proposals EMFF-2019-
1.2.1.1 - Environmental monitoring of ocean energy devices. 50 pp + Annexes.
This communication reflects only the author´s view. CINEA is not responsible for any use that may
be made of the information it contains.
Identification of suitable areas for wave energy
farms
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CONTENTS
1. SAFE WAVE project synopsis ............................................................................... 4
2. Executive summary ............................................................................................. 7
3. Glossary................................................................................................................ 9
4. Introduction ....................................................................................................... 10
5. Objective ........................................................................................................... 13
6. Adoption of a model for the identification of suitable areas for the development
of wave energy projects .......................................................................................... 14
7. Model development ........................................................................................ 16
7.1 Integration of expert knowledge ................................................................. 16
7.1.1 Main outputs of the meeting ................................................................. 17
7.2 Operationalisation of the model.................................................................. 23
7.2.1 Ecological Risk Assessment .................................................................... 24
7.2.2 Technical Assessment............................................................................. 26
7.2.3 Conflicts with other uses......................................................................... 26
7.3 Model feeding ............................................................................................... 30
7.3.1 Description of spatial data .................................................................... 30
7.3.2 Mean Annual Energy Resource ............................................................. 35
7.3.3 WEC performance ................................................................................. 37
8. Implementation of the model into a web-based decision support tool ...... 40
9. Conclusions and future works .......................................................................... 44
10. Bibliography ....................................................................................................... 45
11. ANNEX I. Presentation of DST MSP approach ................................................. 51
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1. SAFE WAVE project synopsis
The European Atlantic Ocean offers a high potential for marine renewable
energy (MRE), which is targeted to be at least 32% of the EU’s gross final
consumption by 2030 (European Commission, 2020(European Commission,
2020). The European Commission is supporting the development of the ocean
energy sector through an array of activities and policies: the Green Deal, the
Energy Union, the Strategic Energy Technology Plan (SET-Plan) and the
Sustainable Blue Economy Strategy. As part of the Green Deal, the Commission
adopted the EU Offshore Renewable Energy Strategy (European Commission,
2020) which estimates to have an installed capacity of at least 60 GW of
offshore wind and at least 1 GW of ocean energy by 2030, reaching 300 GW
and 40 GW of installed capacity, respectively, moving the EU towards climate
neutrality by 2050.
Another important policy initiative is the REPowerEU plan (European
Commission, 2022) which the European Commission launched in response to
Russia’s invasion of Ukraine. REPowerEU plan aims to reduce the European
dependence amongst Member States on Russian energy sources, substituting
fossil fuels by accelerating Europe’s clean energy transition to a more resilient
energy system and a true Energy Union. In this context, higher renewable
energy targets and additional investment, as well as introducing mechanisms
to shorten and simplify the consenting processes (i.e., ‘go-to’ areas or suitable
areas designated by a Member State for renewable energy production) will
enable the EU to fully meet the REPowerEU objectives.
The nascent status of the MRE sector and Wave Energy (WE) in particular, yields
many unknowns about its potential environmental pressures and impacts,
some of them still far from being completely understood. Wave Energy
Converters’ (WECs) operation in the marine environment is still perceived by
regulators and stakeholders as a risky activity, particularly for some groups of
species and habitats.
The complexity of MRE licensing processes is also indicated as one of the main
barriers to the development of the sector. The lack of clarity of procedures
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(arising from the lack of specific laws for this type of projects), the varied
number of authorities to be consulted and the early stage of Marine Spatial
Planning (MSP) implementation are examples of the issues identified that may
delay the permitting of the projects.
Finally, there is also a need to provide more information on the sector not only
to regulators, developers and other stakeholders but also to the general public.
Information should be provided focusing on the ocean energy sector
technical aspects, effects on the marine environment, role on local and
regional socio-economic aspects and effects in a global scale as a sector
producing clean energy and thus having a role in contributing to decarbonise
human activities. Only with an informed society would be possible to carry out
fruitful public debates on MRE implementation at the local level.
These non-technological barriers that could hinder the future development of
WE in EU were addressed by the WESE project funded by EMFF in 2018. The
present project builds on the results of the WESE project and aims to move
forward through the following specific objectives:
1. Development of an Environmental Research Demonstration Strategy based
on the collection, processing, modelling, analysis and sharing of
environmental data collected in WE sites from different European countries
where WECs are currently operating (Mutriku power plant and BIMEP in
Spain, Aguçadoura in Portugal and SEMREV in France); the SafeWAVE
project aims to enhance the understanding of the negative, positive and
negligible effects of WE projects. The SafeWAVE project will continue
previous work, carried out under the WESE project, to increase the
knowledge on priority research areas, enlarging the analysis to other types
of sites, technologies and countries. This will increase information robustness
to better inform decision-makers and managers on real environmental risks,
broad the engagement with relevant stakeholders, related sectors and the
public at large and reduce environmental uncertainties in consenting of WE
deployments across Europe.
2. Development of a Consenting and Planning Strategy through providing
guidance to ocean energy developers and to public authorities tasked with
Identification of suitable areas for wave energy
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consenting and licensing of WE projects in France and Ireland; this strategy
will build on country-specific licensing guidance and on the application of
the MSP decision support tools (i.e. WEC-ERA
1
by Galparsoro et al., 2021
2
and VAPEM
3
tools) developed for Spain and Portugal in the framework of
the WESE project; the results will complete guidance to ocean energy
developers and public authorities for most of the EU countries in the Atlantic
Arch.
Development of a Public Education and Engagement Strategy to work
collaboratively with coastal communities in France, Ireland, Portugal and
Spain, to co-develop and demonstrate a framework for education and public
engagement (EPE) of MRE enhancing ocean literacy and improving the
quality of public debates.
1
https://aztidata.es/wec-era/;
2
Galparsoro, I., M. Korta, I. Subirana, Á. Borja, I. Menchaca, O. Solaun, I. Muxika, G. Iglesias, J.
Bald, 2021. A new framework and tool for ecological risk assessment of wave energy
converters projects. Renewable and Sustainable Energy Reviews, 151: 111539.
https://doi.org/10.1016/j.scitotenv.2022.156037
3
https://aztidata.es/vapem/
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2. Executive summary
The present report describes the process undertaken during the development
of a model for the identification of suitable areas for the development of wave
energy projects in the European Atlantic in the context of maritime spatial
planning and its implementation into a web-based Decision Support Tool.
The approach implemented is based on the previous work developed by
Galparsoro et al. (2020) in the framework of WESE project (Wave Energy in
Southern Europe; Project funded by the European Commission. Agreement
number EASME/EMFF/2017/1.2.1.1/02/SI2.787640). The scope of such project
was the development of a model and a decision support tool for the
identification of the most suitable areas for the development and deploying of
wave energy projects in the Portuguese and Spanish Atlantic area. As the
objective of SafeWAVE is equivalent to that of the WESE project, the same
approach was adopted, but modifications, adaptations and improvements
were applied to fit with the objectives of SafeWAVE. In addition, the
adaptation and improvement of the model was enriched by the consultation
and discussion with WEC industrial developers and scientists. The objective of
the workshop was to share and discuss the approach and assumptions made
during the development and operationalisation of the site suitability model.
The main focus was put on the structure and technical factors considered
within the model. There was a general agreement on that the main factors
were already considered but additional feedback was obtained in relation to
information sources and the way such factors could be integrated into the
model. In particular, regarding the wave energy resource and the estimation
of the production capacity, the oceanographic conditions for construction
and maintenance of the devices, the calculation of the levelized cost of
energy (LCOE), as well as the other aspects related to the deployment of the
farms such as depth, slope, seafloor type, distance to substation and distance
to port.
The conceptual model was then operationalized in a Bayesian Network. The
spatial data to feed the model were obtained from different publicly available
datasets. The geographical scope of the model is the European Atlantic region
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which covers the EEZs of Ireland, the UK, France, Spain and Portugal.
Accounting for a total area of 3,676,970 km2.
The model developed was implemented into a web-based decision support
tool called VAPEM (https://aztidata.es/vapem/) and which was previously
described by Galparsoro et al. (2020).
The model presented here is still subjected to modifications and improvements.
Preliminary results of the suitable areas for development of WEC farms will be
contrasted with WEC developers and scientists to reach a consensus and a
final model that will be used to produce the final suitability maps under Task
6.3.
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3. Glossary
WEC – Wave Energy Converter
DST – Decision Support Tool
MSP – Maritime Spatial Planning
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4. Introduction
The EU has adopted the determination to achieve the climate neutrality by
2050 (European Commission, 2020), by fast forwarding the clean transition and
joining forces to achieve a more resilient energy system (European
Commission, 2022). To achieve such an objective a massive speed-up and
scale-up in renewable energy in power generation is being promoted
(European Commission, 2022). In this context, the marine renewable energy
can become a key player. During the last decades new technologies have
been developed to obtain energy from wind, currents, tides, and waves. In
particular, the global wave energy resource is calculated to be 2.11±0.05 TW
(Gunn and Stock-Williams, 2012), and part of such energy can be harvested
by wave energy converters (WECs) (Sang et al., 2018; Xu et al., 2020),
transforming the kinetic and/or potential energy of waves into electricity
(Lehmann et al., 2017; Mustapa et al., 2017; Stratigaki, 2019). WECs will need to
be deployed in large-scale arrays, forming so-called wave farms (Stratigaki,
2019; Veigas et al., 2015).
Main barriers preventing the development of wave energy converters (WECs)
are: (i) the early stage of development of these technologies, (ii) the
uncertainties regarding the coastal and marine impacts and the risks of wave
farms (Copping et al., 2016; Copping et al., 2020; Hanna et al., 2016), (iii) the
need for a Marine (or Maritime) Spatial Planning (MSP) approach to overcome
the potential competition and conflicts between wave energy sector and
other marine users (O´Hagan, 2016), (iv) the fact that they have been
considered uneconomical (Astariz and Iglesias, 2015), and (v) the slow and
complex consenting process, which is still generally regarded as a non-
technological barrier caused by the complexity and the lack of dedicated
legal frameworks (Apolonia et al., 2021; European Commission, 2022; Simas et
al., 2015).
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In order to support an acceleration of permitting procedures for renewable
energy projects and related infrastructure, the Commission is amending its
proposal on the Renewable Energy Directive
4
. The revised proposal
operationalises the principle of renewable energy as an overriding public
interest, introduces the designation of ‘go-to’ areas and other ways to shorten
and simplify permitting while also minimising potential risks and negative
impacts on the environment (European Commission, 2022). The renewables
‘go-to area’ means a specific location, whether on land or sea, which has
been designated by a Member State as particularly suitable for the installation
of plants for the production of energy from renewable sources, other than
biomass combustion plants. Technical, environmental and socioeconomic
aspects should be taken into account when identifying suitable areas for future
development of marine renewable energy projects, which in turn requires the
adoption of integrative management approaches. The Maritime Spatial
Planning Directive (MSPD) (Directive 2014/89/EU) establishes a framework
aimed at promoting the sustainable growth of maritime economies, the
sustainable development of marine areas and the sustainable use of marine
resources. Marine Spatial Planning (MSP) provides a platform for holistic
assessments and may facilitate the establishment of the marine renewable
energy sector (Hammar et al., 2017; Yates and Bradshaw, 2018). Several
approaches have been proposed in the framework of MSP for the
identification of suitable areas for the development of wave energy projects
(Galparsoro et al., 2012; Maldonado et al., 2022). Basically, they are geo-
spatial multi-criteria evaluation approaches to identify optimal locations to
install a wave energy farm, while minimising potential ecological risks and
conflicts with other coastal and offshore users (Azzellino et al., 2013; Bertram et
al., 2020; Castro-Santos et al., 2019; Flocard et al., 2016; Galparsoro et al., 2012;
4
Proposal for a Directive of the European Parliament and of the Council amending Directive (EU)
2018/2001 of the European Parliament and of the Council as regards the promotion of energy from
renewable sources, COM (2022)222, (18.5.2022)
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Vasileiou et al., 2017). Such approaches aim to assist planners, decision-
makers, industry stakeholders and investors when identifying feasible areas,
based on environmental, technical and socioeconomic criteria.
In this context, the objective of WP6 of SafeWAVE project is the wave energy
development site selection under Maritime Spatial Planning framework. In a
first stage the gathering, editing and management of relevant information was
performed (Task 6.1) (Galparsoro et al., 2021c); while, the present report
focuses into the development of a model for the identification of suitable areas
for the establishment of wave energy projects in the European Atlantic region
in the context of maritime spatial planning and its implementation into a
Decision Support Tool (Task 6.2).
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5. Objective
The main objective of the present deliverable is to describe the process of
development of a model for the identification of suitable areas for the
establishment of wave energy projects in the European Atlantic region in the
context of maritime spatial planning and its and implementation into a web-
based Decision Support Tool (DST).
For the achievement of such purpose, the subsequent steps were conducted.
1. Adoption of a model that was previously developed in the framework of
the Wave Energy in Southern Europe (WESE) project (Galparsoro et al.,
2020).
2. Adaptation of the model to fit with the objectives of SafeWAVE project,
mainly the expansion of the model to the whole European Atlantic
region.
3. Interactions with wave energy converters developers and scientists to
discuss potential improvements of the adopted model.
4. Collation and edition of information layers needed to feed the model.
5. Model feeding.
6. Model runs and evaluation of the results.
7. Integration of the model into a web-based DST, namely VAPEM tool
(https://aztidata.es/vapem/).
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6. Adoption of a model for the identification of
suitable areas for the development of wave
energy projects
The approach implemented in the present task is based on the previous work
developed by Galparsoro et al. (2020) in the framework of WESE project (Wave
Energy in Southern Europe; Project funded by the European Commission.
Agreement number EASME/EMFF/2017/1.2.1.1/02/SI2.787640). The scope of
such project was the definition of a conceptual model and development of a
decision support tool for the identification of the most suitable areas for the
deployment of wave energy projects in the Portuguese and Spanish Atlantic
area. As the objective of SafeWAVE is equivalent to the one of WESE project,
we adopted the same approach, but modifications, adaptations and
improvements were applied to fit with the objectives of Safe WAVE.
Basically, the approach is based on a conceptual model that considers all the
most relevant technical, environmental and conflicting parameters to be
considered when identifying most suitable areas for the development of wave
farms (Galparsoro et al., 2012). The environmental dimension of the model was
defined considering the Marine Strategy Framework Directive (MSFD; Directive
2008/56/EC) for the integrated consideration of the 16 types of pressures and
27 ecosystem elements that could be affected by such technologies
(Galparsoro et al., 2021a). It also takes into consideration other aspects, such
as potential of wave energy resource in the Atlantic area, distribution of other
potentially conflicting maritime activities, legally excluded areas, important
areas for relevant environmental components, and other suitability parameters
(Figure 1).
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7. Model development
7.1 Integration of expert knowledge
The adaptation and improvement of the model was enriched by the
consultation and discussion with WEC industrial developers and scientists.
On the 25th of May 2022 an online workshop was organized by AZTI. The
objective of the workshop was to share the advances made and to discuss
with the industrial partners of SafeWAVE project and other WEC developers the
approach and assumptions made during the development and
operationalisation of the site suitability model. A total number of 10 people
attended to the workshop:
• Patxi Etxaniz (IDOM)
• Enric Villarín (CORPOWER)
• Ines Machado (WAVEC)
• Thomas Soulard (Ecole Centrale de Nantes)
• Enored Le Bourhis (Ecole Centrale de Nantes)
• Laura Zubiate (BiMEP)
• Gotzon Mandiola (AZTI)
• Roland Garnier (AZTI)
• Juan Bald (AZTI)
• Ibon Galparsoro (AZTI)
A presentation was made to guide the discussion (accessible in Annex I of the
present report). During the presentation, discussions were held regarding the
approach, structure and indicators.
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7.1.1 Main outputs of the meeting
7.1.1.1 Model structure and factors considered
First aspect that was presented to the attendees was the structure of the model
that was adopted for further improvement (Figure 2 and Figure 3). There was
an agreement among the attendees that the model suggested, was
considering all the most relevant factors when assessing site suitability.
Figure 2. Simplified representation of the model adopted for the identification of suitable
areas for the development of wave farms. Adapted from Maldonado et al. (2022).
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Figure 3. Bayesian belief model defined for the identification of suitable areas for the
development of wave energy projects. The green box contains all the “environmental
dimension” of the model and considers the environmental pressures (stressors) that might
be produced by wave energy converters, the ecosystem elements that are sensitive to
those pressures (receptors), and corresponding ecological risk. The yellow boxes represent
nodes for which the model is fed with spatially explicit information. The grey box contains
the factors considered under “technical dimension”; and the orange box accounts for the
marine activities that potentially conflict with the establishment of wave farms. The conflicts
are classified as being limiting or excluding activities with the development of wave energy
farms. Adapted from Maldonado et al. (2022).
7.1.1.2 Technical considerations
The main focus was put on the structure and technical factors within the model
(Figure 4). There was a general agreement on the fact that the main factors
were already considered but additional feedbacks were obtained, which are
further developed in the next subsections.
Wave energy resource
The wave energy resource and climatic characteristics are key factor when
identifying suitable areas for the development of wave energy projects.
Originally, the wave energy resource was calculated from data obtained from
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Copernicus Marine Service
5
. The variables used were wave height (Hs) and
peak period (Tp) for a period from 1992 to 2018.
In addition, it was suggested to check the numerical estimation of energy
delivery from a selection of wave energy converters by Todalshaug et al.
(2015). Details on resource code toolbox could be found at:
https://resourcecode.ifremer.fr/tools (with weather windows, wave energy
production and extremes estimations).
Figure 4. Technical factors considered in the Bayesian belief model for the identification of
suitable areas for the development of wave energy projects. Adapted from Maldonado
et al. (2022).
Production capacity
The most important thing is the minimum and maximum energy that can be
obtained at a particular site. It is very difficult to calculate the conversion
capacity of the converter at a site until the device is designed and constructed
according to the characteristics of the selected site. For the model
development perspective, ranges, orders of magnitude of power matrix or
capacity factors for different WECs would be needed.
5
https://marine.copernicus.eu/
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The capacity factor estimation is complex. It is calculated specifically for a
device and the location in which the device will be deployed.
For the model production, it would be very useful to access to annual energy
production (AEP) and capacity factor (CF) for the whole European Atlantic
region.
For wave energy potential, it is need to take time-series of (Hs,Te) over at least
an entire year, convolve it with the power matrix and take the yearly sum.
CorPower agreed to share the CF of their device.
Good weather windows
When considering suitable areas for WECs, one must take the good weather
windows into account to accomplish construction and maintenance works.
Initially, we defined a weather window as the period of five consecutive days
(from 6 am to 6 pm) with significant Hs lower than 1.5 m. Thus, the variable
included in the model is the mean number of weather windows in a year. The
higher the number of weather windows, the greater the technical suitability.
It was suggested that in most cases for maintenance activities, 1 day is enough
and needs to be in daylight. Main issue is that the devices are experimental.
And thus, there is high uncertainty regarding the number of times and
frequency that it is necessary to visit the device. In principle, if everything works
well, it is almost not necessary to go, if it is giving problems, it is necessary to go
periodically, almost every week. It would be interesting to add the probability
per year of having to visit the device, but at this stage of technical
development of the devices, this is a difficult factor to be included in the
model.
It was suggested to check the operation and maintenance characteristics
and costs of offshore wind farms, as this is the sector that is more advanced
and a good reference for wave energy sector. It was suggested to check
Rinaldi et al. (2018).
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Extreme events
Oceanographic extremal events play a relevant role both in the survivability
of the devices, as well as in their performance, but is not considered as a
critical factor to be considered. It could be considered as an economically
limiting factor. WECs are designed considering extremal conditions. It is
expected that extreme events will occur once or twice during the life cycle of
a device. If extreme events occur during the life cycle of the device, it may
break down and stop producing. But if they do not break, they continue to
operate normally. It is not possible at this stage to establish a threshold and the
duration of the extreme conditions.
Levelized Cost of Energy (LCOE)
Due to the nascent state of wave technology, with a wide variety of wave
energy converters (WECs) under development, it is not straightforward to give
a general figure for the WEC cost (Iglesias et al., 2018).
De Andres et al. (2016) investigated the optimum size of WEC for a 20 MW array
based on the CorPower Ocean technology, and a Levelized Cost of Energy
(LCOE) model was created. One plausible option would be a power matrix
that could consider the Aguçadoura testing site.
A recent work by Vanegas-Cantarero et al. (2022) provides a multi-criteria
analysis of CorPower technology that could be used for the model.
Main issue when calculating the LCOE is that the information that is needed to
feed the model needs to be spatially explicit. A good example is provided by
Castro-Santos et al. (2015).
Depth
According to the developers the distance to coastline (or electricity
connection point onshore), is more relevant than the depth itself (in terms of
economic cost). This is because technically, the installation at deep zones is
possible but involves a higher economic expense. Thus, in principle, there is no
depth limitation. At the current status of development of WECs and WEC
projects, it seems unrealistic to go beyond the 100 m depth. Site suitability
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according to depth could be classified according to three depth classes: 20-
30, 30-60, 60-100.
Slope
Seafloor slope was considered in the adopted model. The idea was to avoid
high slope seafloor due to potential instabilities and areas close to continental
shelf break. Seafloor with higher slope than 3 degrees would be avoided. No
comments and no objection to the approach was made.
Seafloor type
The model prioritizes sedimentary seafloor, because the installation is easier
and cheaper, using gravity or drag anchors. Nevertheless, it should be
considered that there are different designs and solutions also for rocky seafloor
(thus, it is mainly an economic factor). Due to present status of development
and other costs associated to the WECs installation, for the present version of
the model, rocky seafloor will be avoided but not excluded.
Seafloor score
The seafloor score is obtained as the combination of to the bathymetry, slope,
and seafloor type (rock or sediment). Sedimentary seafloor is preferred to rocky
ones; however, the latter are not automatically excluded. Deep locations
(depth > 200 meters) are not directly excluded but are less preferable due to
higher potential costs of farm construction. Therefore, since the seafloor
characteristics are determinant when identifying suitable locations, the
proposed seafloor score can be used to filter unfeasible locations (too shallow
or too steep). The seafloor score is used to prioritise sedimentary seafloor, but if
the slope is low, rocky seafloor could also be suitable.
Onshore network connection
The model takes the distance to the nearest onshore electric network
connection into account since this factor is of high relevance when estimating
the cost of construction of the wave energy farm. Therefore, the nearer the
connection, the greater the technical suitability. Distance higher than 60 km
should be high.
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23
Distance to port
The distance to port is a relevant factor that impacts on the installation and
maintenance costs of the farm. Distance to different types of ports should be
considered according to the project phase:
o For manufacturing and construction, the port should be large and
equipped with major loading and unloading systems. It should have a
shipyard.
o For maintenance operations, a small port is more than enough.
Considering the high number of small ports in European coastline and that
we do not have a full list of ports, a good proxy might be to consider the
distance to coastline.
As an example, in Portugal, the current farms are 4 to 5 km away, and the first
one was 6 km away.
CO2 intensity
It was suggested adding CO2 intensity as a technical factor in the next version
of the tool. It might not yet be a criteria used for WECs but it is expected that it
will by the time WEC become commercial. A low score could be close to CO2
intensity of nuclear or wind, and high compared to gaz.
7.2 Operationalisation of the model
The conceptual model was then operationalised using the Bayesian Belief
Network (BBN) approach. The BBN approach is suitable for the integration of
the conceptual model, as it makes it possible to integrate empirical information
and expert knowledge when empirical information is not available
(Maldonado et al., 2022).
The model integrates environmental, technical, and social dimensions (Figure
1). Figure 2 shows a simplified representation of the wave energy model, in
which the type of node regarding the origin of the data (expert or real) is
specified. Real data was used to feed the spatially explicit nodes, whereas
expert knowledge was intended to determine the non-spatial nodes.
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7.2.1 Ecological Risk Assessment
The ecological risk dimension of the model was adopted from Galparsoro et
al. (2021a), which was operationalised by Galparsoro et al. (2021b) and
Maldonado et al. (2022).
Ecological risk can be described as the likelihood and magnitude of adverse
effects from stressors to ecological receptors (Figure 5). ERAs (Hope, 2006)
could be used to link a certain operational activity with the vulnerability of
ecosystem components and with the occurrence and magnitude of pressures
that such activity could pose in the environment. To assess the total risk and
the identification of management measures that could be adopted for
minimising the potential environmental impact.
Figure 5. General framework implemented for the Ecological Risk Assessment. From
Galparsoro et al. (2021a).
Figure 6 shows the piece of BBN involving the environmental dimension, i.e.,
the pressures produced by each technology, the sensitivity of different
environmental components (e.g., mammals, marine birds, seafloor, etc.) to
these pressures and their risk.
Identification of suitable areas for wave
energy farms
25
Figure 6. Environmental dimension of the Bayesian model implemented for the identification
of suitable áreas for the development of wave farms. Blue nodes represent each of the
pressures potentially produced by a wave energy converter, while the green nodes
represent the sensitivity of each of the ecosystem components to each pressure and the
estimated risk (red). The model calculates the total pressures and the total environmental
risks (violet). Adapted from Maldonado et al. (2022).
Identification of suitable areas for wave
energy farms
26
7.2.2 Technical Assessment
The technical dimension of the model is also based on Galparsoro et al.
(2021b) and Maldonado et al. (2022), but it was adapted according to
feedbacks obtained from wave energy converters developers.
The current wave energy technology has a number of requirements to select
a suitable location, including the depth or seafloor type. Moreover, the wave
power (resource) is an important factor to consider when determining
technically feasible locations. Other factors considered in this model are the
distances to coast, nearest port, and nearest electrical substation. Figure 7
shows the piece of BBN that reflects this technical assessment.
Figure 7. Technical dimension of the Bayesian model implemented for the identification of
suitable areas for the development of wave farms.
7.2.3 Conflicts with other uses
The interaction of different activities taking place in the same location is a
challenge that needs to be tackled when planning suitable locations for new
Identification of suitable areas for wave
energy farms
27
wave energy farms. In this regard, 18 excluding and 4 limiting factors are
considered in the model (Figure 8). Excluding factors are incompatible
activities or uses of the space, such as marine protected areas, bathing waters
or dredging areas. Limiting factors are compatible but limiting activities, such
as maritime traffic or fishing. Figure 8 shows the piece of BBN that represents
possible conflicts between the infrastructure of WECs and the economic
activities considered.
Figure 8. Bayesian model that considers the potential conflicts of wave energy farms with
other marine uses.
Finally, Figure 9 shows the full BBN model constructed. Interactions between
multiple stressors are combined using several pathways. Blue nodes represent
the pressure types potentially produced by a WEC. Green nodes represent the
sensitivity of different ecosystem components to such pressures; while the red
nodes, represent the ecological risk of the project to each individual
ecosystem element. All of these result into the total ecological risk (violet
boxes).
Identification of suitable areas for wave
energy farms
28
Besides, the grey-coloured nodes represent the risks associated to the
technical dimension based on seven parameters. The orange nodes represent
the conflicts with other sectors classified as being limiting or excluding. Finally,
the final integrated feasibility is obtained as a result of the combination of the
environmental, technical feasibility and conflicts with other uses.
Identification of suitable areas for wave energy farms
29
Figure 9. Structure of the full Bayesian network model for the identification of suitable areas for the development of wave energy farms. The final
node, integrated feasibility results from the combination of the total environmental risk, technical feasibility, and conflicts with other (limiting
or excluding) activities. Adapted from Maldonado et al. (2022).
Identification of suitable areas for wave
energy farms
30
7.3 Model feeding
All the variables in the wave energy model are discrete, or have been
discretized, and both real data and expert knowledge were used. Real
data was used to feed the spatially explicit nodes, for instance, the wave
power, whereas expert knowledge was intended to determine the non-
spatial nodes, such as the pressures produced by a WEC.
7.3.1 Description of spatial data
The spatial data were obtained from different publicly available datasets.
The geographical scope of the model is the European Atlantic region
which covers the EEZs of Ireland, UK, France, Spain and Portugal (Figure
10)
6
. The total area of the study area is 3,676,970 km2. A reference grid of
1 km resolution is used as the unit of observation, which is based on the
ETRS89 Lambert Azimuthal Equal Area coordinate reference system. The
first stage of the WP6 was the identification, gathering, editing and
management of relevant information for identifying suitable areas for the
development of wave energy projects (Galparsoro et al., 2021c). The
identified sources of information dealt with:
o Technical aspects such as wave energy resource, depth, seafloor type
distribution, distance to ports and good weather windows. Those
factors are of high relevance for the identification of suitable areas in
terms of their technical viability.
o Legal constraints representing the spatial distribution of areas that
could be under different management and legal restrictions that
could affect the development or establishment of wave energy
facilities.
o Environmental aspects for the consideration of the potential
ecological risk that the establishment or development of a wave farm
may have.
6
Obtained from https://www.marineregions.org/
Identification of suitable areas for wave
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31
o Maritime activities and uses that potentially could conflict or pose
limitations to the development or establishment of marine wave
energy facilities.
The full list of spatially explicit variables, description and discretization
values used for the Bayesian Network development is shown in Table 1.
It should be noted that the process of generation of relevant information
for site suitability will be a continuous process throughout the SafeWAVE
project.
Figure 10. Spatial extension of the analysis area in the European Atlantic region which
considers the exclusive economic zones of Ireland, UK, France, Spain and Portugal3.
Identification of suitable areas for wave energy farms
32
Table 1 List of spatially explicit variables, description and discretization values used for the Bayesian Network development.
Node set
Node name
Description
Discretization
Environmental component
Fish presence
Species (elasmobranchii and actinopterygii) richness (nº
of species per square kilometre), based on predictions of
occurrence. Source Aquamaps.org
Computed as quantile 90
H: [475, 800]
L: [54, 475)
Environmental component
Mammal presence
Species richness (nº of species per square kilometre),
based on predictions of occurrence of mammal
species. Source Aquamaps.org
Computed as quantile 90
H: [9, 13]
L: [0, 9)
Environmental component
Reptile presence
Species richness (nº of species per square kilometre),
based on predictions of occurrence. Source
Aquamaps.org
Computed as quantile 90
H: [3, 4]
L: [0, 3)
Environmental component
Bird presence
Breeding bird species richness (nº of species per square
kilometre), based on predictions of occurrence. Source
Aquamaps.org
Computed as quantile 90
H: [18, 25]
L: [0, 18)
Environmental component
Cephalopod
presence
Species richness (nº of species per square kilometre),
based on predictions of occurrence. Source
Aquamaps.org
Computed as quantile 90
H: [67, 78]
L: [16, 67)
Environmental component
Seabed presence
Presence of sensitive habitats
Y: Presence of sensitive habitats
N: otherwise (or unknown)
Technical component
Seafloor type
Most frequent seabed substrate type in a cell
Rocky: Rock or other hard substrate
majority
Sedimentary: Non rocky substrate
majority
Technical component
Wave power
Mean wave power (MWh/m) in a cell
N: [0, 217)
L: [217, 282)
M: [282, 331)
H: [331, 401]
Identification of suitable areas for wave
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Node set
Node name
Description
Discretization
Technical component
Depth
Mean depth in a cell (meters)
Deep: [-200, -100)
Medium: [-100, -30)
Shallow: [-30, 0]
Technical component
Distance to port
Distance from centre of cell to nearest port, avoiding
land (km)
L: [0, 10)
M: [10, 30)
H: [30, 600]
Technical component
Distance to
electrical
substations
Straight distance from centre of cell to nearest electrical
substation
L: [0, 30) km
M: [30, 100) km
H: [100, 600] km
Human activities
Cable
Presence/absence of cables (buffer 500 m)
Y: presence of cables
N: otherwise (or unknown)
Human activities
Dredging
Presence/absence of dredging sites (buffer 500 m)
Y: presence of dredging sites
N: otherwise (or unknown)
Human activities
Aggregates
extraction areas
Presence/absence of aggregates extraction areas
(buffer 500 m)
Y: presence of aggregates extraction
areas
N: otherwise (or unknown)
Human activities
Ports
Presence/absence of ports (buffer 1 km)
Y: presence of ports
N: otherwise (or unknown)
Human activities
Ocean energy
devices
Presence/absence of “in development” or
“operational” ocean energy devices (buffer 500 m)
Y: presence of devices
N: otherwise (or unknown)
Human activities
Oil and gas
Presence/absence of areas of “exploitation” or
“exploration” of oil or gas
Y: presence of oil or gas areas
N: otherwise (or unknown)
Human activities
Pipelines
Presence/absence of pipelines (buffer 500 m)
Y: presence of pipelines
N: otherwise (or unknown)
Human activities
Dredge spoil
dumping
Presence/absence of dredge spoil dumping sites (buffer
500 m)
Y: presence of dredge soil dumping sites
N: otherwise (or unknown)
Human activities
Dumped munitions
Presence/absence of dumped munitions sites (buffer
500 m)
Y: presence of dumped munition sites
N: otherwise (or unknown)
Identification of suitable areas for wave
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Node set
Node name
Description
Discretization
Human activities
Urban waste
discharge
Presence/absence of urban waste discharge sites
(buffer 500 m)
Y: presence of urban waste discharge
points
N: otherwise (or unknown)
Human activities
Bathing waters
Evaluation of bathing waters. Possible values are
excellent, good, sufficient, poor and not evaluated
Y: bathing waters have been evaluated
N: otherwise
Human activities
Aquaculture
Presence/absence of fish or shellfish aquaculture sites
(buffer 500 m)
Y: presence of aquaculture sites
N: otherwise (or unknown)
Human activities
Protected areas
Presence/absence of marine protected areas (MPAs)
incompatible with other activities
Y: presence of MPAs
N: otherwise (or unknown)
Human activities
Strictly protected
areas
Presence/absence of marine strictly protected areas
(strictly MPAs) incompatible with other activities
Y: presence of strictly MPAs
N: otherwise (or unknown)
Human activities
Sea motorway
Presence/absence of sea motorways
Y: presence of sea motorways
N: otherwise (or unknown)
Human activities
Cultural heritage
Presence/absence of cultural heritage sites
Y: presence of cultural heritage sites
N: otherwise (or unknown)
Human activities
Militar area
Presence/absence of military area
Y: presence of military area
N: otherwise (or unknown)
Human activities
Tourism
Presence/absence of touristic sites
Y: presence of touristic sites
N: otherwise (or unknown)
Human activities
Fishing effort
Number of fishing vessels above 15 m length
L: [2, 7)
H: [7, 1690]
Human activities
Maritime traffic
Vessel density (Hours/km2*moth)
L: [0, 2.8)
H: [2.8, 2.2*104)
Identification of suitable areas for wave
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7.3.2 Mean Annual Energy Resource
Wave data:
The wave reanalysis data presented by Maldonado et al. (2022) have
been extended to the new domain covering the European Atlantic
region. Furthermore, the data have been updated with the 2022
database, that contains data for the period 1993-2020 (Maldonado et al.,
2022 used data for the period 1993-2019). Another improvement with
respect to the Maldonado et al., 2022 study, is about the spatial resolution.
Most of the wave data were collected from the IBI-MFC Multi-Year (MY)
high-resolution wave reanalysis product provided by CMEMS
(IBI_REANALYSIS_WAV_005_006, hourly data). The reanalysis is based on
the MFWAM model developed by Meteo-France (MF), fed by the ERA 5
reanalysis wind data from ECMWF. and has a horizontal resolution of 0.05º
(the reanalysis used by Maldonado et al. (2022) had a resolution of 0.1º).
This dataset covers the IBI (Iberian Biscay Irish) area (latitude [-19º, 5º],
longitude [26º, 56º]). It includes the offshore islands of Spain and Portugal,
except Azores (Canary Islands, Balearic Islands, Madeira). For Azores,
wave data were downloaded from the global wave reanalysis product
provided by CMEMS (WAVERYS, GLOBAL_REANALYSIS_WAV_001_032),
obtained with the same MFWAM model forced by the ERA5 wind field,
with a horizontal resolution of 0.2º and a temporal resolution of 3 hours (like
Maldonado et al., 2022). The following spectral sea state parameters have
been used: significant wave height (Hs), mean period (Tm), peak period
(Tp), mean direction (Dm) and peak direction (Dp). Similarly, to
Maldonado et al. (2022), only the points located at depth lower than 200
m were used.
Energy resource:
The energy resource is obtained by means of linear wave theory. The
wave power (P) reads:
36
where the wave energy (E) is expressed as:
where ρ (Rho) is the density of seawater (assumed to be 1025 kg/m3) and
g is the gravitational acceleration.
The wave group celerity (cg) is expressed as:
where L is the wavelength, Te is the energy period, k is the wave number
and h the water depth.
Te can be estimated by means of the spectral moments if available.
However, if the spectral shape is unknown (as in the present case)
approximations should be used. In the present study, it is assumed that the
sea states follow a standard JONSWAP spectrum with a peak
enhancement factor of γ = 3.3, which allows to simplify the following
relation Te ≈ 0.9Tp representative of regions with combined presence of
sea and swell sea states (Cornett, 2008; Gonçalves et al., 2014; Sierra et
al., 2016). Despite that this assumption introduces some uncertainty in the
estimation of P, it should be taken into account that it is proportional to Te
and to the square of Hs, so the errors induced by inaccurate Te are less
significant than the errors induced by inaccurate Hs.
Finally, the annual energy resource (AER, in MWh/m) for a given year is
obtained by the following expression:
where f is the occurrence of the corresponding bin at the locations of
interest. Then, we obtain the mean annual energy resource (MAER, in
MWh/m), by averaging AER over the period of analysis (1993-2020):
37
Figure 11 shows MAER obtained with the global reanalysis WAVERYS (0.2º
resolution, left, this covers the whole European Atlantic region) and with
the IBI-MFC reanalysis (0.05º resolution, right).
Figure 11. Mean annual energy resource (MAER, in MWh/m).
7.3.3 WEC performance
In this study, we give information of the performance of a specific device
through the computation of the mean power production (MPP, in kW), the
capacity factor (CF, in %) and the capture width (CW, in m).
The mean power production (MPP, in kW) is defined as follows:
where PM is the power matrix of the specific device and represents the
power that can be delivered for different wave conditions (Hs, Te), where
the power is given in kW.
The capacity factor (CF, in %) relates the mean power production with the
nominal power given of the device given by the constructor (Pmax):
38
The capture width (CW, in m) relates the power production and the
energy resource and is defined as:
where MAEP (in MWh) is the mean annual energy production defined as:
The following figures show the variables of interest obtained with the
global reanalysis WAVERYS (0.2º resolution, left, this covers the whole
European Atlantic region) and with the IBI-MFC reanalysis (0.05º resolution,
right). They show preliminary results obtained for a generic power matrix.
In the next steps, we will use real power matrix provided by our partners.
Figure 12. Mean annual energy production (in MWh)
39
Figure 13. Capacity factor obtained for generic device (CF, in %).
Figure 14. Capture width of a generic device (CW, in m).
40
8. Implementation of the model into a web-based
decision support tool
The model developed and described in this report was implemented into
a web-based decision support tool called VAPEM and that was previously
described by Galparsoro et al. (2020).
The tool was developed in Shiny (https://shiny.rstudio.com), a package of
R (https://cran.r-project.org) programming open source language that
facilitates the creation of interactive web applications. This tool represents
an interactive interface between the model and the user. The tool permits
the user to define, explore and visualise the results of different scenarios,
being a spatially explicit tool.
Within the main menu of the tool, the WECs feasibility model developed in
SafeWAVE project is accessible by the user (Figure 15).
Once the WECs feasibility model has been selected, the user can explore
different pre-defined scenarios which define different environmental,
socio-economic or technical factors that could influence in the feasibility
of different geographical locations for the development of wave energy
projects (Figure 16), or the user can generate their own scenario by
assigning different values to the parameters of the wave energy model
that define the feasibility for the development of wave energy projects
(Figure 17).
The user can also visualize and explore the full model and analyse the
consequences of changing values (evidence) of the different factors
influencing the final feasibility of the development of wave energy
projects (Figure 18). Finally, the user can visualize the results of its own
scenario as bar charts (Figure 19) or as a map representing the
geographical distribution of the feasibility for wave energy projects
development (Figure 20). The user has the option of visualising the final
feasibility map and the background maps of factors influencing the final
feasibility value. The tool also provides some visualisation modifications
options (map colours, etc.), as well as the option of downloading the final
map as a GIS layer.
41
Figure 15. Front page of the VAPEM tool in which the Wave Energy Converters feasibility
model has been integrated (marked with the yellow box).
Figure 16. Screenshot with the pre-defined scenarios when running the Wave Energy
Converters feasibility model.
42
Figure 17. Screenshot of the panel in which the user can define its own scenarios for the
identification of feasible areas for the development of wave energy projects.
Figure 18. Visualization of the full Wave Energy Converters feasibility model. The user can
change the values of the factors influencing the feasibility of wave energy projects.
43
Figure 19. Bar chart visualization of the feasibility for wave energy projects development
based on input values and definition of scenarios.
Figure 20. Integrated feasibility map for wave energy projects development.
44
9. Conclusions and future works
The main objective of Task 6.2 was the development of a model for the
identification of suitable areas for the construction of wave energy
projects in the European Atlantic region in the context of maritime spatial
planning and its implementation into a Decision Support Tool.
The developed model is based on previous work but adapted to cover
the whole European Atlantic region and has been improved by the
incorporation of modifications proposed by wave energy converters
developers and scientists of the SafeWAVE partnership.
The final objective of WP6 is the identification of suitable areas for the
development of wave energy projects, and thus, the expected final
product of this WP is a set of maps that could be used for the identification
of the most suitable locations. The next months will be dedicated to the
production of the maps (Task 6.3), which will require to continue working
on potential adjustments of the model according to the feedback of
scientist and wave energy converters developers. The present version of
the model will probably require additional modifications to reach a
consensus and a final model that will be used to produce the final
suitability maps.
45
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